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These studies suggest that stratified random sampling improves precision and efficiency in estimating population parameters, enhances performance in specific tasks like gene classification and image categorization, and is more economically feasible compared to unrestricted random sampling.
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Stratified random sampling is a statistical technique used to improve the precision of estimates by dividing a population into distinct subgroups, or strata, and then sampling from each stratum. This method is particularly useful when the population has heterogeneous characteristics that can be grouped into homogeneous strata. The technique aims to ensure that each subgroup is adequately represented in the sample, thereby reducing sampling error and increasing the efficiency of the estimates.
Improved Precision and Efficiency:
Application in Clinical Trials:
Variance Optimization in Streaming Data:
Feature Subspace Selection in High Dimensional Data:
Empirical Likelihood Inference:
Sample Allocation Methods:
Double Sampling Procedure:
Multi-label Data Stratification:
Stratified random sampling is a powerful technique that enhances the precision and efficiency of statistical estimates by ensuring adequate representation of subgroups within a population. It is particularly beneficial in clinical trials, high-dimensional data contexts, and when auxiliary information is available. Various algorithms and methods, such as Neyman allocation and double sampling, further optimize the process, making stratified random sampling a versatile tool in both theoretical and practical applications.
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